





'''simple docstring'''


import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 				UpperCAmelCase_    (		__lowerCamelCase   :   Union[str, Any]      ,__lowerCamelCase   :   Dict      ,__lowerCamelCase   :   int      ,__lowerCamelCase   :   Union[str, Any]      ,__lowerCamelCase   :   Tuple					):
   lowercase_     :List[Any]            =					TapasConfig.from_json_file(a__					)
   # set absolute/relative position embeddings parameter
   lowercase_     :Dict            =					reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      lowercase_     :str            =					TapasForQuestionAnswering(config=a__					)
   elif task == "WTQ":
      # run_task_main.py hparams
      lowercase_     :Optional[Any]            =					4
      lowercase_     :Union[str, Any]            =					True
      # hparam_utils.py hparams
      lowercase_     :str            =					0.664_694
      lowercase_     :List[Any]            =					0.207_951
      lowercase_     :Optional[int]            =					0.121_194
      lowercase_     :List[Any]            =					True
      lowercase_     :Tuple            =					True
      lowercase_     :Any            =					False
      lowercase_     :List[str]            =					0.0_352_513

      lowercase_     :Optional[Any]            =					TapasForQuestionAnswering(config=a__					)
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      lowercase_     :Dict            =					4
      lowercase_     :Dict            =					False
      # hparam_utils.py hparams
      lowercase_     :Any            =					36.4_519
      lowercase_     :List[Any]            =					0.903_421
      lowercase_     :Union[str, Any]            =					222.088
      lowercase_     :List[Any]            =					True
      lowercase_     :Optional[int]            =					True
      lowercase_     :str            =					True
      lowercase_     :Optional[int]            =					0.763_141

      lowercase_     :Any            =					TapasForQuestionAnswering(config=a__					)
   elif task == "TABFACT":
      lowercase_     :List[Any]            =					TapasForSequenceClassification(config=a__					)
   elif task == "MLM":
      lowercase_     :Tuple            =					TapasForMaskedLM(config=a__					)
   elif task == "INTERMEDIATE_PRETRAINING":
      lowercase_     :Any            =					TapasModel(config=a__					)
   else:
      raise ValueError(F'Task {task} not supported.'					)

   print(F'Building PyTorch model from configuration: {config}'					)
   # Load weights from tf checkpoint
   load_tf_weights_in_tapas(a__      ,a__      ,a__					)

   # Save pytorch-model (weights and configuration)
   print(F'Save PyTorch model to {pytorch_dump_path}'					)
   model.save_pretrained(a__					)

   # Save tokenizer files
   print(F'Save tokenizer files to {pytorch_dump_path}'					)
   lowercase_     :Tuple            =					TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"      ,model_max_length=5_12					)
   tokenizer.save_pretrained(a__					)

   print("Used relative position embeddings:"      ,model.config.reset_position_index_per_cell					)


if __name__ == "__main__":
 lowerCAmelCase   :	Optional[Any]   				=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
 )
 parser.add_argument(
     '''--reset_position_index_per_cell''',
     default=False,
     action='''store_true''',
     help='''Whether to use relative position embeddings or not. Defaults to True.''',
 )
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--tapas_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained TAPAS model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 lowerCAmelCase   :	Tuple   				=parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case				:      Optional[int]   		=			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLMForMultipleChoice""",
	    """XLMForQuestionAnswering""",
	    """XLMForQuestionAnsweringSimple""",
	    """XLMForSequenceClassification""",
	    """XLMForTokenClassification""",
	    """XLMModel""",
	    """XLMPreTrainedModel""",
	    """XLMWithLMHeadModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLMForMultipleChoice""",
	    """TFXLMForQuestionAnsweringSimple""",
	    """TFXLMForSequenceClassification""",
	    """TFXLMForTokenClassification""",
	    """TFXLMMainLayer""",
	    """TFXLMModel""",
	    """TFXLMPreTrainedModel""",
	    """TFXLMWithLMHeadModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	__snake_case				:      Union[str, Any]   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


